The emerging concern of IMP variants being resistant to the only IMP-type metallo-β-lactamase inhibitor, xeruborbactam
Bibliographic record
Abstract
ABSTRACT Metallo-β-lactamases (MBLs) of IMP type are not inhibited by currently commercialized β-lactamase inhibitors, including taniborbactam (TAN), which inhibits only NDM- and VIM-type enzymes. However, the development of xeruborbactam (XER), which additionally inhibits IMP enzymes, may provide effective drug combinations such as meropenem-XER (MEM-XER) against most MBL producers. Thirty-two IMP-producing clinical gram-negative isolates were tested for MEM-XER. Susceptibility testing of β-lactams with TAN or XER at 4 or 8 µg/mL was performed. Noticeably, MEM-XER remained ineffective against all IMP-producing Pseudomonas aeruginosa isolates. By contrast, supplementation with XER significantly lowered MEM MICs for several IMP-producing Enterobacterales isolates, except for isolates and recombinant E. coli strains producing IMP-6, IMP-10, IMP-14, and IMP-26. Interestingly, IMP-59 producers showed susceptibility to both TAN- and XER-based combinations, although IMP enzymes are not supposed to be inhibited by TAN. Determinations of 50% inhibitory concentration (IC 50 ) values of XER showed values being >15-fold higher for IMP-6, IMP-10, IMP-14, and IMP-26 compared with IMP-1. Interestingly, the IC 50 value of TAN for IMP-59 was found in the same range as that for NDM-1 (7 µM). Finally, structural analyses and molecular modeling simulations indicated that the Ser262Gly mutation in IMP-6 may alter the electronic properties of the active site, whereas the Phe residue in IMP-10 may exert a steric effect counteracting XER binding. Resistance to XER in IMP-6, IMP-10, IMP-14, and IMP-26 variants, conferring resistance to MEM-XER, might be considered a serious concern since MEM-XER will be supposed to be a salvage therapy for MBL-, and especially IMP-producing Enterobacterales infections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".